Various types of biometric systems are used more and more in order to provide for increased security for accessing an electronic device, thereby providing an enhanced user convenience. In particular fingerprint sensors have been successfully integrated in such devices, for example, thanks to their small form factor, high performance and user acceptance. Among the various available fingerprint sensing principles (such as capacitive, optical, thermal etc.), capacitive sensing is most commonly used, in particular in applications where size and power consumption are important issues.
All capacitive fingerprint sensors provide a measure indicative of the capacitance between several sensing elements and a finger placed on the surface of the fingerprint sensor. Acquisition of a fingerprint image is typically performed using a fingerprint sensor comprising a plurality of sensing elements arranged in a two-dimensional manner, and a block based technique may be applied to the fingerprint sensor for acquiring a fingerprint image, where the blocks of sensing elements are sampled sequentially. As an example, a block of eight sensing elements adjacently arranged in one row may be sampled at the same time.
The presence of noise in the sensor introduces an error into the data values that are read when sampling each block of sensing elements. This error manifests as a potentially varying offset from a certain zero-offset reference, such as ground. Because the blocks of sensing elements are scanned sequentially and because the amount of noise in the sensor may vary over time, a different error may occur in each block of sensing elements.
This noise problem has traditionally been compensated by configuring the hardware of the fingerprint sensor. However, a software or firmware approach may be advantageous as the amount of compensation can be flexibly controlled. Additionally, the software or firmware approach does not consume additional silicon area or silicon development schedule, and is relatively computationally inexpensive.
An exemplary software implementation for noise reduction is disclosed in US 2014/0015774 A1, where the acquired sensor data is adjusted in order to compensate for noise introduced by the fingerprint sensor. According to US 2014/0015774 A1, a redundant sensing element is introduced in regards to the block based sampling of the fingerprint sensor, where the same redundant sensing element will be sampled by each one of two sequentially sampled blocks. An offset is calculated based on a difference between the redundant sampling of the same sampling element, and the second block is adjusted based on the calculated difference.
Even though US 2014/0015774 A1 introduces an interesting approach to noise reduction when applying a block based sensing technique, the disclosed approach will largely rely on firstly acquired sample on each row and thus the overall result will be largely dependent on the validity of this first sample. Thus, there appears to be room for further improvement in regards to software based noise mitigation technique to be applied to a fingerprint image.